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A frequency-velocity CNN for developing near-surface 2D vs images from linear-array, active-source wavefield measurements

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dc.contributor.authorAbbas, Aseren
dc.contributor.authorVantassel, Joseph P.en
dc.contributor.authorCox, Brady R.en
dc.contributor.authorKumar, Krishnaen
dc.contributor.authorCrocker, Jodieen
dc.date.accessioned2023-03-24T18:52:12Zen
dc.date.available2023-03-24T18:52:12Zen
dc.date.issued2023-04en
dc.description.abstractThis paper presents a frequency-velocity convolutional neural network (CNN) for rapid, non-invasive 2D shear wave velocity (VS) imaging of near-surface geo-materials. Operating in the frequency-velocity domain allows for significant flexibility in the linear-array, active-source experimental testing configurations used for generating the CNN input, which are normalized dispersion images. While normalized dispersion images retain the most important aspects of near-surface wavefields, they are relatively insensitive to the exact experimental testing configuration used to generate and record the wavefields, accommodating various source types, source offsets, numbers of receivers, and receiver spacings. We demonstrate the effectiveness of the frequency-velocity CNN by applying it to a common near-surface geophysics problem, namely, imaging a two-layer, undulating, soil-over -bedrock interface. The frequency-velocity CNN was trained and tested using 100,000 synthetic near-surface models with variable soil-over-bedrock conditions. Then, the ability of the frequency-velocity CNN to gener-alize across various acquisition configurations was rigorously tested using thousands of synthetic near-surface models with different acquisition configurations from that of the training set. Lastly, it was applied to experi-mental field data collected at the Hornsby Bend site in Austin, Texas, USA and found to produce a subsurface 2D image that was in great agreement with ground truth from invasive site characterization data.en
dc.description.notesThe open-source software DENISE (Kohn 2011; Kohn et al., 2012) was used for all the wave propagation simulations conducted in this study. The Texas Advanced Computing Center's (TACC's) cluster Stampede2 was used in the construction of seismic wavefield-image pairs, with an allocation provided by DesignSafe-CI (Rathje et al., 2017) . Google Colaboratory along with the open-source machine learning library Keras (Chollet et al., 2015) were used in training andtesting the CNNs presented herein. The wavefield transformations were performed using the open-source Python package swprocess (Vantas-sel 2021) . Matplotlib 3.1.2 (Hunter, 2007) was used to create the figures in this study. This work was supported primarily by the U.S. National Science Foundation (NSF) grant CMMI-2120155 with equipment re-sources for field testing at the Hornsby Bend site associated with grant CMMI-2037900. However, any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.en
dc.description.sponsorshipU.S. National Science Foundation (NSF) [CMMI-2120155, CMMI-2037900]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.compgeo.2023.105305en
dc.identifier.eissn1873-7633en
dc.identifier.other105305en
dc.identifier.urihttp://hdl.handle.net/10919/114172en
dc.identifier.volume156en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectMachine learningen
dc.subjectCNNen
dc.subjectSubsurface imagingen
dc.subjectSurface wavesen
dc.subjectInsitu testingen
dc.subjectGeophysical testingen
dc.titleA frequency-velocity CNN for developing near-surface 2D vs images from linear-array, active-source wavefield measurementsen
dc.title.serialComputers and Geotechnicsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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